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hu_core_news_lg / edit_tree_lemmatizer.py
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from functools import lru_cache
from typing import cast, Any, Callable, Dict, Iterable, List, Optional
from typing import Sequence, Tuple, Union
from collections import Counter
from copy import deepcopy
from itertools import islice
import numpy as np
import srsly
from thinc.api import Config, Model, SequenceCategoricalCrossentropy, NumpyOps
from thinc.types import Floats2d, Ints2d
from spacy.pipeline._edit_tree_internals.edit_trees import EditTrees
from spacy.pipeline._edit_tree_internals.schemas import validate_edit_tree
from spacy.pipeline.lemmatizer import lemmatizer_score
from spacy.pipeline.trainable_pipe import TrainablePipe
from spacy.errors import Errors
from spacy.language import Language
from spacy.tokens import Doc, Token
from spacy.training import Example, validate_examples, validate_get_examples
from spacy.vocab import Vocab
from spacy import util
TOP_K_GUARDRAIL = 20
default_model_config = """
[model]
@architectures = "spacy.Tagger.v2"
[model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v2"
pretrained_vectors = null
width = 96
depth = 4
embed_size = 2000
window_size = 1
maxout_pieces = 3
subword_features = true
"""
DEFAULT_EDIT_TREE_LEMMATIZER_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
"trainable_lemmatizer_v2",
assigns=["token.lemma"],
requires=[],
default_config={
"model": DEFAULT_EDIT_TREE_LEMMATIZER_MODEL,
"backoff": "orth",
"min_tree_freq": 3,
"overwrite": False,
"top_k": 1,
"overwrite_labels": True,
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
},
default_score_weights={"lemma_acc": 1.0},
)
def make_edit_tree_lemmatizer(
nlp: Language,
name: str,
model: Model,
backoff: Optional[str],
min_tree_freq: int,
overwrite: bool,
top_k: int,
overwrite_labels: bool,
scorer: Optional[Callable],
):
"""Construct an EditTreeLemmatizer component."""
return EditTreeLemmatizer(
nlp.vocab,
model,
name,
backoff=backoff,
min_tree_freq=min_tree_freq,
overwrite=overwrite,
top_k=top_k,
overwrite_labels=overwrite_labels,
scorer=scorer,
)
# _f = open("lemmatizer.log", "w")
# def debug(*args):
# _f.write(" ".join(args) + "\n")
def debug(*args):
pass
class EditTreeLemmatizer(TrainablePipe):
"""
Lemmatizer that lemmatizes each word using a predicted edit tree.
"""
def __init__(
self,
vocab: Vocab,
model: Model,
name: str = "trainable_lemmatizer",
*,
backoff: Optional[str] = "orth",
min_tree_freq: int = 3,
overwrite: bool = False,
top_k: int = 1,
overwrite_labels,
scorer: Optional[Callable] = lemmatizer_score,
):
"""
Construct an edit tree lemmatizer.
backoff (Optional[str]): backoff to use when the predicted edit trees
are not applicable. Must be an attribute of Token or None (leave the
lemma unset).
min_tree_freq (int): prune trees that are applied less than this
frequency in the training data.
overwrite (bool): overwrite existing lemma annotations.
top_k (int): try to apply at most the k most probable edit trees.
"""
self.vocab = vocab
self.model = model
self.name = name
self.backoff = backoff
self.min_tree_freq = min_tree_freq
self.overwrite = overwrite
self.top_k = top_k
self.overwrite_labels = overwrite_labels
self.trees = EditTrees(self.vocab.strings)
self.tree2label: Dict[int, int] = {}
self.cfg: Dict[str, Any] = {"labels": []}
self.scorer = scorer
self.numpy_ops = NumpyOps()
def get_loss(
self, examples: Iterable[Example], scores: List[Floats2d]
) -> Tuple[float, List[Floats2d]]:
validate_examples(examples, "EditTreeLemmatizer.get_loss")
loss_func = SequenceCategoricalCrossentropy(normalize=False, missing_value=-1)
truths = []
for eg in examples:
eg_truths = []
for (predicted, gold_lemma, gold_pos, gold_sent_start) in zip(
eg.predicted,
eg.get_aligned("LEMMA", as_string=True),
eg.get_aligned("POS", as_string=True),
eg.get_aligned_sent_starts(),
):
if gold_lemma is None:
label = -1
else:
form = self._get_true_cased_form(
predicted.text, gold_sent_start, gold_pos
)
tree_id = self.trees.add(form, gold_lemma)
# debug(f"@get_loss: {predicted}/{gold_pos}[{gold_sent_start}]->{form}|{gold_lemma}[{tree_id}]")
label = self.tree2label.get(tree_id, 0)
eg_truths.append(label)
truths.append(eg_truths)
d_scores, loss = loss_func(scores, truths)
if self.model.ops.xp.isnan(loss):
raise ValueError(Errors.E910.format(name=self.name))
return float(loss), d_scores
def predict(self, docs: Iterable[Doc]) -> List[Ints2d]:
if self.top_k == 1:
scores2guesses = self._scores2guesses_top_k_equals_1
elif self.top_k <= TOP_K_GUARDRAIL:
scores2guesses = self._scores2guesses_top_k_greater_1
else:
scores2guesses = self._scores2guesses_top_k_guardrail
# The behaviour of *_scores2guesses_top_k_greater_1()* is efficient for values
# of *top_k>1* that are likely to be useful when the edit tree lemmatizer is used
# for its principal purpose of lemmatizing tokens. However, the code could also
# be used for other purposes, and with very large values of *top_k* the method
# becomes inefficient. In such cases, *_scores2guesses_top_k_guardrail()* is used
# instead.
n_docs = len(list(docs))
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
n_labels = len(self.cfg["labels"])
guesses: List[Ints2d] = [self.model.ops.alloc2i(0, n_labels) for _ in docs]
assert len(guesses) == n_docs
return guesses
scores = self.model.predict(docs)
assert len(scores) == n_docs
guesses = scores2guesses(docs, scores)
assert len(guesses) == n_docs
return guesses
def _scores2guesses_top_k_equals_1(self, docs, scores):
guesses = []
for doc, doc_scores in zip(docs, scores):
doc_guesses = doc_scores.argmax(axis=1)
doc_guesses = self.numpy_ops.asarray(doc_guesses)
doc_compat_guesses = []
for i, token in enumerate(doc):
tree_id = self.cfg["labels"][doc_guesses[i]]
form: str = self._get_true_cased_form_of_token(token)
if self.trees.apply(tree_id, form) is not None:
doc_compat_guesses.append(tree_id)
else:
doc_compat_guesses.append(-1)
guesses.append(np.array(doc_compat_guesses))
return guesses
def _scores2guesses_top_k_greater_1(self, docs, scores):
guesses = []
top_k = min(self.top_k, len(self.labels))
for doc, doc_scores in zip(docs, scores):
doc_scores = self.numpy_ops.asarray(doc_scores)
doc_compat_guesses = []
for i, token in enumerate(doc):
for _ in range(top_k):
candidate = int(doc_scores[i].argmax())
candidate_tree_id = self.cfg["labels"][candidate]
form: str = self._get_true_cased_form_of_token(token)
if self.trees.apply(candidate_tree_id, form) is not None:
doc_compat_guesses.append(candidate_tree_id)
break
doc_scores[i, candidate] = np.finfo(np.float32).min
else:
doc_compat_guesses.append(-1)
guesses.append(np.array(doc_compat_guesses))
return guesses
def _scores2guesses_top_k_guardrail(self, docs, scores):
guesses = []
for doc, doc_scores in zip(docs, scores):
doc_guesses = np.argsort(doc_scores)[..., : -self.top_k - 1 : -1]
doc_guesses = self.numpy_ops.asarray(doc_guesses)
doc_compat_guesses = []
for token, candidates in zip(doc, doc_guesses):
tree_id = -1
for candidate in candidates:
candidate_tree_id = self.cfg["labels"][candidate]
form: str = self._get_true_cased_form_of_token(token)
if self.trees.apply(candidate_tree_id, form) is not None:
tree_id = candidate_tree_id
break
doc_compat_guesses.append(tree_id)
guesses.append(np.array(doc_compat_guesses))
return guesses
def set_annotations(self, docs: Iterable[Doc], batch_tree_ids):
for i, doc in enumerate(docs):
doc_tree_ids = batch_tree_ids[i]
if hasattr(doc_tree_ids, "get"):
doc_tree_ids = doc_tree_ids.get()
for j, tree_id in enumerate(doc_tree_ids):
if self.overwrite or doc[j].lemma == 0:
# If no applicable tree could be found during prediction,
# the special identifier -1 is used. Otherwise the tree
# is guaranteed to be applicable.
if tree_id == -1:
if self.backoff is not None:
doc[j].lemma = getattr(doc[j], self.backoff)
else:
form = self._get_true_cased_form_of_token(doc[j])
lemma = self.trees.apply(tree_id, form) or form
# debug(f"@set_annotations: {doc[j]}/{doc[j].pos_}[{doc[j].is_sent_start}]->{form}|{lemma}[{tree_id}]")
doc[j].lemma_ = lemma
@property
def labels(self) -> Tuple[int, ...]:
"""Returns the labels currently added to the component."""
return tuple(self.cfg["labels"])
@property
def hide_labels(self) -> bool:
return True
@property
def label_data(self) -> Dict:
trees = []
for tree_id in range(len(self.trees)):
tree = self.trees[tree_id]
if "orig" in tree:
tree["orig"] = self.vocab.strings[tree["orig"]]
if "subst" in tree:
tree["subst"] = self.vocab.strings[tree["subst"]]
trees.append(tree)
return dict(trees=trees, labels=tuple(self.cfg["labels"]))
def initialize(
self,
get_examples: Callable[[], Iterable[Example]],
*,
nlp: Optional[Language] = None,
labels: Optional[Dict] = None,
):
validate_get_examples(get_examples, "EditTreeLemmatizer.initialize")
if self.overwrite_labels:
if labels is None:
self._labels_from_data(get_examples)
else:
self._add_labels(labels)
# Sample for the model.
doc_sample = []
label_sample = []
for example in islice(get_examples(), 10):
doc_sample.append(example.x)
gold_labels: List[List[float]] = []
for token in example.reference:
if token.lemma == 0:
gold_label = None
else:
gold_label = self._pair2label(token.text, token.lemma_)
gold_labels.append(
[
1.0 if label == gold_label else 0.0
for label in self.cfg["labels"]
]
)
gold_labels = cast(Floats2d, gold_labels)
label_sample.append(self.model.ops.asarray(gold_labels, dtype="float32"))
self._require_labels()
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(label_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample, Y=label_sample)
def from_bytes(self, bytes_data, *, exclude=tuple()):
deserializers = {
"cfg": lambda b: self.cfg.update(srsly.json_loads(b)),
"model": lambda b: self.model.from_bytes(b),
"vocab": lambda b: self.vocab.from_bytes(b, exclude=exclude),
"trees": lambda b: self.trees.from_bytes(b),
}
util.from_bytes(bytes_data, deserializers, exclude)
return self
def to_bytes(self, *, exclude=tuple()):
serializers = {
"cfg": lambda: srsly.json_dumps(self.cfg),
"model": lambda: self.model.to_bytes(),
"vocab": lambda: self.vocab.to_bytes(exclude=exclude),
"trees": lambda: self.trees.to_bytes(),
}
return util.to_bytes(serializers, exclude)
def to_disk(self, path, exclude=tuple()):
path = util.ensure_path(path)
serializers = {
"cfg": lambda p: srsly.write_json(p, self.cfg),
"model": lambda p: self.model.to_disk(p),
"vocab": lambda p: self.vocab.to_disk(p, exclude=exclude),
"trees": lambda p: self.trees.to_disk(p),
}
util.to_disk(path, serializers, exclude)
def from_disk(self, path, exclude=tuple()):
def load_model(p):
try:
with open(p, "rb") as mfile:
self.model.from_bytes(mfile.read())
except AttributeError:
raise ValueError(Errors.E149) from None
deserializers = {
"cfg": lambda p: self.cfg.update(srsly.read_json(p)),
"model": load_model,
"vocab": lambda p: self.vocab.from_disk(p, exclude=exclude),
"trees": lambda p: self.trees.from_disk(p),
}
util.from_disk(path, deserializers, exclude)
return self
def _add_labels(self, labels: Dict):
if "labels" not in labels:
raise ValueError(Errors.E857.format(name="labels"))
if "trees" not in labels:
raise ValueError(Errors.E857.format(name="trees"))
self.cfg["labels"] = list(labels["labels"])
trees = []
for tree in labels["trees"]:
errors = validate_edit_tree(tree)
if errors:
raise ValueError(Errors.E1026.format(errors="\n".join(errors)))
tree = dict(tree)
if "orig" in tree:
tree["orig"] = self.vocab.strings[tree["orig"]]
if "orig" in tree:
tree["subst"] = self.vocab.strings[tree["subst"]]
trees.append(tree)
self.trees.from_json(trees)
for label, tree in enumerate(self.labels):
self.tree2label[tree] = label
def _labels_from_data(self, get_examples: Callable[[], Iterable[Example]]):
# Count corpus tree frequencies in ad-hoc storage to avoid cluttering
# the final pipe/string store.
vocab = Vocab()
trees = EditTrees(vocab.strings)
tree_freqs: Counter = Counter()
repr_pairs: Dict = {}
for example in get_examples():
for token in example.reference:
if token.lemma != 0:
form = self._get_true_cased_form_of_token(token)
# debug("_labels_from_data", str(token) + "->" + form, token.lemma_)
tree_id = trees.add(form, token.lemma_)
tree_freqs[tree_id] += 1
repr_pairs[tree_id] = (form, token.lemma_)
# Construct trees that make the frequency cut-off using representative
# form - token pairs.
for tree_id, freq in tree_freqs.items():
if freq >= self.min_tree_freq:
form, lemma = repr_pairs[tree_id]
self._pair2label(form, lemma, add_label=True)
@lru_cache()
def _get_true_cased_form(self, token: str, is_sent_start: bool, pos: str) -> str:
if is_sent_start and pos != "PROPN":
return token.lower()
else:
return token
def _get_true_cased_form_of_token(self, token: Token) -> str:
return self._get_true_cased_form(token.text, token.is_sent_start, token.pos_)
def _pair2label(self, form, lemma, add_label=False):
"""
Look up the edit tree identifier for a form/label pair. If the edit
tree is unknown and "add_label" is set, the edit tree will be added to
the labels.
"""
tree_id = self.trees.add(form, lemma)
if tree_id not in self.tree2label:
if not add_label:
return None
self.tree2label[tree_id] = len(self.cfg["labels"])
self.cfg["labels"].append(tree_id)
return self.tree2label[tree_id]